{"id":"W2066247257","doi":"10.1515/cog-2013-0008","title":"Extracting prototypes from exemplars What can corpus data tell us about concept representation?","year":2013,"lang":"en","type":"article","venue":"Cognitive Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Abstraction; Categorization; Computer science; Natural language processing; Representation (politics); Cognitive linguistics; Artificial intelligence; Basis (linear algebra); Computational linguistics; Cognition; Cluster analysis; Linguistics; Psychology; Mathematics; Epistemology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00939535,0.0005046137,0.000845805,0.004242306,0.001004508,0.005262183,0.001811583,0.001739852,0.004026708],"category_scores_gemma":[0.09120176,0.00063959,0.0007920064,0.004631008,0.003316774,0.009459942,0.002460301,0.001794541,0.0009879571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007733311,"about_ca_system_score_gemma":0.0006321043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391642,"about_ca_topic_score_gemma":0.001316216,"domain_scores_codex":[0.9942111,0.003581128,0.0003935304,0.0009710703,0.0007120197,0.0001311978],"domain_scores_gemma":[0.9316971,0.04606768,0.003022975,0.01498163,0.003749593,0.0004809075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001793223,0.0005305392,0.2133413,0.002660299,0.001179781,0.00109875,0.0146008,0.04371103,0.02337227,0.1227828,0.01587286,0.5590563],"study_design_scores_gemma":[0.000173001,0.0004278746,0.0984536,0.001349058,0.0003020577,0.001286296,0.01259371,0.2452097,0.01614701,0.5804914,0.04313475,0.0004315123],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7336144,0.0020603,0.2418785,0.003717071,0.0002129491,0.0001989911,0.004516592,0.0009603691,0.01284083],"genre_scores_gemma":[0.9058875,0.000512141,0.08937068,0.0002023266,0.00006675798,0.0002231372,0.003170994,0.0001398101,0.000426692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00939535,"threshold_uncertainty_score":0.04968792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04349772500190396,"score_gpt":0.3351766878981312,"score_spread":0.2916789628962272,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}